Visualising the future Smart Grids

Fri, Sep 1, 2017
2 min read

Background

Modern Smart Grids rely on advanced computational tools to analyse the security of the system and provide valuable information to the network operators. However, it is often hard for human beings to quickly make sense of the huge flow of information generated by the computation techniques. Thus, a huge effort is invested to building appropriate Human-Machine-Interface tools to convey the information to the network operators in a fast, clear, and easily understandable way. The most successful of these tools rely on visualisation techniques as these can convey huge amount of information in a small period.

One of the main computational tools used by system operators to assess the security of Smart Grids are time-domain dynamic simulations. These tools build a mathematical model of the Smart Grid1 and then simulate the system time evolution after a disturbance.

Schematic of Nordic grid

Objectives

In this project, you have to design a visualisation platform for time-domain dynamic simulations. This platform will use real-time simulation data from the dynamic simulator RAMSES2 to update a heatmap of the Smart Grid network representation. This will require interfacing with the dynamic simulator, getting the appropriate network information, and updating accordingly a heatmap overlayed over the network schematic. The Nordic[^Nordic] test system will be used to test the technique, however the platform should be flexible to allow for the use of different systems. Some existing libraries can be used or modified to achieve this (e.g., 3).

Deliverables

A complete literature review including a comparison between different existing solutions. This should cover Human-Machine-Interface solutions in Smart Grids and more specifically visualisation techniques.

A visualisation platform (preferably written in Python) that overlays a heatmap over the schematic representation of a power system and updates it based on data inflow from RAMSES.

All the code developed should be documented and published on GitHub under an MIT License4. The final code (along with all other supplementary files) should be published on Zenodo and the DOI included in the final report5.

Student profile

Good programming skills (Python, or willingness to learn in a short period)